Characteristics of COVID-19 patients admitted to a tertiary care hospital in Pune, India, and cost-effective predictors of intensive care treatment requirement

Background

Maharashtra is one of the worst affected states in this pandemic. ² As of 30th September, Maharashtra has in total 1.4 million cases with 38,000 deaths. Objective was to study associations of severity of disease and need for ICU treatment in COVID-19 patients.

Methods

A retrospective study of clinical course in 800 hospitalized COVID-19 patients, and a predictive model of need for ICU treatment. Eight hundred consecutive patients admitted with confirmed COVID-19 disease.

Results

Average age was 41 years, 16% were <20 years of age, 55% were male, 50% were asymptomatic and 16% had at least one comorbidity. Using MoHFW India severity guidelines, 73% patients had mild, 6% moderate and 20% severe disease. Severity was associated with higher age, symptomatic presentation, elevated neutrophil and reduced lymphocyte counts and elevated inflammatory markers. Seventy-seven patients needed ICU treatment: they were older (56 years), more symptomatic and had lower SpO2 and abnormal chest X-ray and deranged hematology and biochemistry at admission. A model trained on the first 500 patients, using above variables predicted need for ICU treatment with sensitivity 80%, specificity 88% in subsequent 300 patients; exclusion of expensive laboratory tests did not affect accuracy.

Conclusion

In the early phase of COVID- 19 epidemic, a significant proportion of hospitalized patients were young and asymptomatic. Need for ICU treatment was predicted by simple measures including higher age, symptomatic onset, low SpO2 and abnormal chest X-ray. We propose a cost-effective model for referring patients for treatment at specialized COVID-19 hospitals.

Key Messages

• Of 800 patients, 73% had mild, 6% moderate and 20% had severe disease.

• Seventy-seven patients (9.6%) required ICU treatment, 25 (3%) died.

• ICU treatment was predicted by higher age, more symptomatic presentation, lower SpO2 and pneumonia on chest X-ray at admission.

• A machine learning model features in first 500 patients accurately predicted ICU treatment in subsequent 300 patients.

• A good clinical protocol, SpO2 and chest X-ray are adequate to predict and triage COVID-19 patients for hospital admissions in resource poor environments.